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      03 数据存储与划分原则
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  <time class="dt-published" datetime="2018-01-04T13:56:09.000Z" itemprop="datePublished">2018年01月04日</time>
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        <p>前两节对分库分表和遇到一些问题进行解释和总结，本节对分库分表的数据存储和划分原则进行一个讲解</p>
<h5 id="数据存储"><a href="#数据存储" class="headerlink" title="数据存储"></a>数据存储</h5><p>分别是：</p>
<ol>
<li>独立存储+缓存：适用于数据量少，基本不变的数据；</li>
<li>读写分离 ： 适用于数据量适中，增长平缓，读多写少的数据</li>
<li>水平切分： 适用于数据量大，增长快速，读写频繁的数据</li>
</ol>
<h5 id="划分原则"><a href="#划分原则" class="headerlink" title="划分原则"></a>划分原则</h5><ol>
<li>能不分就不分，当单表记录达到一定数量级（&gt;1000万）之后才考虑进行水平切分处理；</li>
<li>分片字段取决于最频繁的查询SQL，选择合适的切分规则，避免跨库聚合，跨库事务；</li>
<li>分片数量尽量少，分片尽量均匀分布在多个 DataHost 上，一个查询 SQL 跨分片越多，则总体性能越差；</li>
<li>考虑数据的增长模式，数据的访问模式，分片关联性能问题，分片扩容问题；</li>
<li>分片表要避免不带任何where条件的查询；<span id="more"></span></li>
</ol>
<h5 id="分片规则"><a href="#分片规则" class="headerlink" title="分片规则"></a>分片规则</h5><p>下面对常用的分片进行一个介绍</p>
<h6 id="范围约定"><a href="#范围约定" class="headerlink" title="范围约定"></a>范围约定</h6><p><img src="https://wz-blogimg.oss-cn-beijing.aliyuncs.com/img/WEB470831992412c350e3eeb52ec3c72b0b.png" alt="image"></p>
<ul>
<li><p>优点：此分片规则在扩容时只需要添加节点，指定日期范围，可以避免扩容时的数据迁移,</p>
<p>  例如：表test规划了两个节点分别为db1、db2，db1存储2015.12之前的数据，db2存储2016.1-2016.6的数据，当时间达到2016.6以后，现有分片规则无法满足，所以就面临分片的增加，一般分片的增加需要进行数据迁移，而基于范围的分片规则，只需增加2016.7 -2016.12即可，不需要对以前的数据进行迁移。</p>
</li>
<li><p>缺点：负载不均衡</p>
<p>  如2015.12以前这个业务发展不是特别快，数据量相对不大，但是以后随着营销和推广，业务飞速发展，那么2016.1-2016.6这个节点数据量就相对比较大。</p>
</li>
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<h6 id="取模"><a href="#取模" class="headerlink" title="取模"></a>取模</h6><p><img src="https://wz-blogimg.oss-cn-beijing.aliyuncs.com/img/WEBd630d46ad3b571954f88399362fdfd30.png" alt="image"></p>
<blockquote>
<p>根据分片键的值进行hash，并根据节点数进行取余</p>
</blockquote>
<ul>
<li>优点：负载均衡</li>
<li>缺点：在扩容时需要全量迁移</li>
</ul>
<h6 id="一致性Hash"><a href="#一致性Hash" class="headerlink" title="一致性Hash"></a>一致性Hash</h6><p><img src="https://wz-blogimg.oss-cn-beijing.aliyuncs.com/img/WEB9d2ad3069263f96953e124d88e1689a8.png" alt="image"></p>
<blockquote>
<p>按照常用的hash算法来将对应的key哈希到一个具有2^32次方个桶的空间中，即0~(2^32)-1的数字空间中。现在我们可以将这些数字头尾相连，想象成一个闭合的环形.</p>
</blockquote>
<ol>
<li>首先求出数据库节点的哈希值，并将其配置到0-2^32圆上，如<ol>
<li>Hash(NODE1) &#x3D; KEY1&#x3D;2^10;</li>
<li>Hash(NODE2) &#x3D; KEY2&#x3D;2^20;</li>
<li>Hash(NODE3) &#x3D; KEY3&#x3D;2^30;</li>
</ol>
</li>
<li>然后采用同样的方法求出存储数据的键的哈希值，并映射到相同的圆上。</li>
<li>然后从数据映射到的位置开始顺时针查找，将数据保存到找到的第一个服务器上。如果超过2^32仍然找不到服务器，则从0开始查找。<ol start="4">
<li>示例：<br> 根据用户ID分片，流水号规则为UR+12位序号,如UR000000000013, 进行hash计算为2^5，则属于NODE1</li>
</ol>
</li>
</ol>
<ul>
<li>优点：负载相对均衡</li>
<li>缺点：在扩容时需要部分迁移</li>
</ul>
<p>​    </p>

        
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    <h3 class="widget-title"><i class="fa fa-toc"></i> 文章目录</h3>
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      <ol class="toc"><li class="toc-item toc-level-5"><a class="toc-link" href="#%E6%95%B0%E6%8D%AE%E5%AD%98%E5%82%A8"><span class="toc-text">数据存储</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#%E5%88%92%E5%88%86%E5%8E%9F%E5%88%99"><span class="toc-text">划分原则</span></a></li><li class="toc-item toc-level-5"><a class="toc-link" href="#%E5%88%86%E7%89%87%E8%A7%84%E5%88%99"><span class="toc-text">分片规则</span></a><ol class="toc-child"><li class="toc-item toc-level-6"><a class="toc-link" href="#%E8%8C%83%E5%9B%B4%E7%BA%A6%E5%AE%9A"><span class="toc-text">范围约定</span></a></li><li class="toc-item toc-level-6"><a class="toc-link" href="#%E5%8F%96%E6%A8%A1"><span class="toc-text">取模</span></a></li><li class="toc-item toc-level-6"><a class="toc-link" href="#%E4%B8%80%E8%87%B4%E6%80%A7Hash"><span class="toc-text">一致性Hash</span></a></li></ol></li></ol>
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              <p>原文链接: <a href="https://dongweizhao.github.io/2018/01/04/2022/数据库中间件/数据存储与划分原则/">https://dongweizhao.github.io/2018/01/04/2022/数据库中间件/数据存储与划分原则/</a></p>
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